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Procedia Manufacturing 00 (2017) 000–000
www.elsevier.com/locate/procedia
* Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 741
E-mail address: psafonso@dps.uminho.pt
2351-9789 © 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017.
Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June
2017, Vigo (Pontevedra), Spain
Costing models for capacity optimization in Industry 4.0: Trade-off
between used capacity and operational efficiency
A. Santana
a, P. Afonso
a,*, A. Zanin
b, R. Wernke
ba University of Minho, 4800-058 Guimarães, Portugal bUnochapecó, 89809-000 Chapecó, SC, Brazil
Abstract
Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected, information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of maximization. The study of capacity optimization and costing models is an important research topic that deserves contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency.
© 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017.
Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency 1. Introduction
The cost of idle capacity is a fundamental information for companies and their management of extreme importance in modern production systems. In general, it is defined as unused capacity or production potential and can be measured in several ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity
Procedia Manufacturing 31 (2019) 142–147
2351-9789 © 2019 The Authors. Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the 9th Conference on Learning Factories. 10.1016/j.promfg.2019.03.022
10.1016/j.promfg.2019.03.022 2351-9789
© 2019 The Authors. Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the 9th Conference on Learning Factories. Available online at www.sciencedirect.com
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
9th Conference on Learning Factories 2019
Implementation of a cyber-physical cooling storage station in a
learning factory
Marcus Vogt
a,∗, Benjamin Uhlig
a, Kuldip Singh Sangwan
b, Christoph Herrmann
a,
Sebastian Thiede
aaTechnische Universität Braunschweig, Institute of Machine Tools and Production Technology, Sustainable Manufacturing and Life Cycle Engineering, Langer Kamp 19 b, Braunschweig, 38106, Germany
bBirla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani 333031, Rajasthan, India
Abstract
Learning factories are established means for learning production and process-engineering relevant topics and improving holistic system understanding. Learning factories integrate real-world applications into small-scaled factories to teach students, employees or researchers. Connecting the physical world with virtual (cyber) models to develop cyber-physical systems has become attractive due to low cost, high performance IT infrastructure. However, learning factories and cyber-physical systems have been rarely combined. In this paper, a cyber-physical cooling storage station is presented, which is integrated into an existing learning factory and its potential for engineering education is analysed. In addition, an innovative visualisation enables user interaction for learners. This system allows learners to experience the interaction of thermodynamic processes, industrial sensors and industrial automation to deepen their knowledge in laboratory exercises.
c
2019 The Authors. Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the 9thConference on Learning Factories.
Keywords:
Action and research-based learning; cyber-physical systems; energy efficiency; learning factories; thermoelectric cooling storage station
1. Introduction
The creation of cyber-physical production systems (CPPS) is a major future trend in industry as well as research; and is an essential element of the fourth industrial revolution [1, 2]. Therefore, the early introduction of engineering students to CPPS is becoming an indispensable element for the engineering education. Learning factories provide suitable learning environment [3], especially to overcome possible social and technological barriers [4]. Some processes and process chains have already been implemented in learning factories, but their focus is mainly on manufacturing processes [5]. Cooling processes have not yet been extensively introduced and taught in the context of CPPS in learning factories [6]. Cooling processes are not only very important in industry, but also very
∗Corresponding author. Tel.: +49 531 391-7622 ; fax: +49 531 391-5842. E-mail address: marcus.vogt@tu-braunschweig.de
2351-9789 c 2019 The Authors. Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the 9thConference on Learning Factories. Available online at www.sciencedirect.com
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
9th Conference on Learning Factories 2019
Implementation of a cyber-physical cooling storage station in a
learning factory
Marcus Vogt
a,∗, Benjamin Uhlig
a, Kuldip Singh Sangwan
b, Christoph Herrmann
a,
Sebastian Thiede
aaTechnische Universität Braunschweig, Institute of Machine Tools and Production Technology, Sustainable Manufacturing and Life Cycle Engineering, Langer Kamp 19 b, Braunschweig, 38106, Germany
bBirla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani 333031, Rajasthan, India
Abstract
Learning factories are established means for learning production and process-engineering relevant topics and improving holistic system understanding. Learning factories integrate real-world applications into small-scaled factories to teach students, employees or researchers. Connecting the physical world with virtual (cyber) models to develop cyber-physical systems has become attractive due to low cost, high performance IT infrastructure. However, learning factories and cyber-physical systems have been rarely combined. In this paper, a cyber-physical cooling storage station is presented, which is integrated into an existing learning factory and its potential for engineering education is analysed. In addition, an innovative visualisation enables user interaction for learners. This system allows learners to experience the interaction of thermodynamic processes, industrial sensors and industrial automation to deepen their knowledge in laboratory exercises.
c
2019 The Authors. Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the 9thConference on Learning Factories.
Keywords:
Action and research-based learning; cyber-physical systems; energy efficiency; learning factories; thermoelectric cooling storage station
1. Introduction
The creation of cyber-physical production systems (CPPS) is a major future trend in industry as well as research; and is an essential element of the fourth industrial revolution [1, 2]. Therefore, the early introduction of engineering students to CPPS is becoming an indispensable element for the engineering education. Learning factories provide suitable learning environment [3], especially to overcome possible social and technological barriers [4]. Some processes and process chains have already been implemented in learning factories, but their focus is mainly on manufacturing processes [5]. Cooling processes have not yet been extensively introduced and taught in the context of CPPS in learning factories [6]. Cooling processes are not only very important in industry, but also very
∗Corresponding author. Tel.: +49 531 391-7622 ; fax: +49 531 391-5842. E-mail address: marcus.vogt@tu-braunschweig.de
2351-9789 c 2019 The Authors. Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the 9thConference on Learning Factories.
2 M. Vogt et al. / Procedia Manufacturing 00 (2019) 000–000
energy-intensive. Conditioning of buildings, industrial processes and mobile cooling are some major applications of cooling processes to ensure a closed cooling chain [7–9]. In general, operators and other stakeholders are strongly interested in improving energy efficiency and reliability in order to save costs [10] and reduce environmental impact. In practice, cooling systems often do not provide comprehensive information transparency and automated decision support is rarely possible. Here, CPPS approaches can be beneficial. The digital representation of the reality leads to greater transparency and automated decision support [11].
This paper presents a cyber-physical cooling storage station, called fridge demonstrator (FRED), which is inte-grated into an existing learning factory. Furthermore, the potential of FRED for engineering education and possible applications for teaching are analysed. FRED is not only capable of cooling down small work pieces, but can also store and retrieve them. Moreover, students can integrate their own modelling approaches to predict the cooling times. In summary, working with the system learners have the chance to learn more about thermodynamic processes, industrial sensors and industrial automation and deepen their knowledge in laboratory exercises.
2. Cyber-physical production systems (CPPS) in learning factories
Due to the rapid development of the information and communication technology (ICT), a large number of its appli-cations in industry and research have become possible [4]. In general, a CPPS consists of four main elements: physical world, data acquisition, cyber world, and visualisation & control. To be at par with the advancements of the ICT, em-ployees and the future manufacturing engineers need to be educated adequately [3]. To impart key competencies for this development, learning factories are seen as suitable learning environments [3]. Numerous authors consider en-ergy efficiency to be a meaningful field taught in learning factories [12–14], however, literature reveals that enen-ergy efficiency is rarely taught against the background of digitalisation in production engineering [15]. The present CPPS demonstrates the use of learning factories for teaching and research by integrating energy efficiency and resource effi-ciency. CPPSs are successful in transferring knowledge to students, which is in accordance with the blended learning concept [16]. In this concept, students actively participate in hands-on education [17], which leads to higher learning motivation and success [18]. At Technische Universität Braunschweig, concepts for the design of learning environ-ments for teaching energy efficiency and energy transparency have already been developed [19]. However, to the best of authors’ knowledge, currently there is no example covering topics of energy efficiency and transparency in practical courses and team projects. Therefore, the development of such production processes is important.
3. Concept of FRED
Motivation and learning success of engineering students can be improved by combining research-based learning approaches and utilisation of physical infrastructure [18]. "Die Lernfabrik" at Technische Universität Braunschweig follows this approach. This learning factory has three parts Research Lab, Experience Lab and Education Lab. The practical part of the course "Sustainable Cyber Physical Production Systems" takes place in the Experience Lab and focusses on the fundamentals of CPPS and its application, in order to foster sustainability in production engineer-ing [4]. This paper presents the development of a refrigerated storage station, meanengineer-ing a fridge demonstrator (FRED), which is integrated into the Experience Lab. The suitability of FRED for education in learning factories is evaluated using the assessment framework introduced by Thiede et al. [4]. The authors of this framework conclude that there are three distinct levels (I to III) of CPPS. A CPPS of level III has the highest degree of suitability for educational purposes. Applying this framework, the suitability of a technical system for teaching can be assessed. Each of the four elements of a CPPS is evaluated. A CPPS can reach a maximum of twelve points, implying a high suitability for education.
3.1. Implementation of a CPPS
The CPPS elements of FRED are shown in figure 1 (b). Starting from the physical world, the CPPS performs two main tasks: first, cool down the work pieces (WP) within a certain amount of time and second, store the work pieces in a certain position on the trays of the storage rack. To achieve the first task, thermoelectric cooling units, peltier
Marcus Vogt et al. / Procedia Manufacturing 31 (2019) 142–147 143
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
9th Conference on Learning Factories 2019
Implementation of a cyber-physical cooling storage station in a
learning factory
Marcus Vogt
a,∗, Benjamin Uhlig
a, Kuldip Singh Sangwan
b, Christoph Herrmann
a,
Sebastian Thiede
aaTechnische Universität Braunschweig, Institute of Machine Tools and Production Technology, Sustainable Manufacturing and Life Cycle Engineering, Langer Kamp 19 b, Braunschweig, 38106, Germany
bBirla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani 333031, Rajasthan, India
Abstract
Learning factories are established means for learning production and process-engineering relevant topics and improving holistic system understanding. Learning factories integrate real-world applications into small-scaled factories to teach students, employees or researchers. Connecting the physical world with virtual (cyber) models to develop cyber-physical systems has become attractive due to low cost, high performance IT infrastructure. However, learning factories and cyber-physical systems have been rarely combined. In this paper, a cyber-physical cooling storage station is presented, which is integrated into an existing learning factory and its potential for engineering education is analysed. In addition, an innovative visualisation enables user interaction for learners. This system allows learners to experience the interaction of thermodynamic processes, industrial sensors and industrial automation to deepen their knowledge in laboratory exercises.
c
2019 The Authors. Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the 9thConference on Learning Factories.
Keywords:
Action and research-based learning; cyber-physical systems; energy efficiency; learning factories; thermoelectric cooling storage station
1. Introduction
The creation of cyber-physical production systems (CPPS) is a major future trend in industry as well as research; and is an essential element of the fourth industrial revolution [1, 2]. Therefore, the early introduction of engineering students to CPPS is becoming an indispensable element for the engineering education. Learning factories provide suitable learning environment [3], especially to overcome possible social and technological barriers [4]. Some processes and process chains have already been implemented in learning factories, but their focus is mainly on manufacturing processes [5]. Cooling processes have not yet been extensively introduced and taught in the context of CPPS in learning factories [6]. Cooling processes are not only very important in industry, but also very
∗Corresponding author. Tel.: +49 531 391-7622 ; fax: +49 531 391-5842. E-mail address: marcus.vogt@tu-braunschweig.de
2351-9789 c 2019 The Authors. Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the 9thConference on Learning Factories.
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
9th Conference on Learning Factories 2019
Implementation of a cyber-physical cooling storage station in a
learning factory
Marcus Vogt
a,∗, Benjamin Uhlig
a, Kuldip Singh Sangwan
b, Christoph Herrmann
a,
Sebastian Thiede
aaTechnische Universität Braunschweig, Institute of Machine Tools and Production Technology, Sustainable Manufacturing and Life Cycle Engineering, Langer Kamp 19 b, Braunschweig, 38106, Germany
bBirla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani 333031, Rajasthan, India
Abstract
Learning factories are established means for learning production and process-engineering relevant topics and improving holistic system understanding. Learning factories integrate real-world applications into small-scaled factories to teach students, employees or researchers. Connecting the physical world with virtual (cyber) models to develop cyber-physical systems has become attractive due to low cost, high performance IT infrastructure. However, learning factories and cyber-physical systems have been rarely combined. In this paper, a cyber-physical cooling storage station is presented, which is integrated into an existing learning factory and its potential for engineering education is analysed. In addition, an innovative visualisation enables user interaction for learners. This system allows learners to experience the interaction of thermodynamic processes, industrial sensors and industrial automation to deepen their knowledge in laboratory exercises.
c
2019 The Authors. Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the 9thConference on Learning Factories.
Keywords:
Action and research-based learning; cyber-physical systems; energy efficiency; learning factories; thermoelectric cooling storage station
1. Introduction
The creation of cyber-physical production systems (CPPS) is a major future trend in industry as well as research; and is an essential element of the fourth industrial revolution [1, 2]. Therefore, the early introduction of engineering students to CPPS is becoming an indispensable element for the engineering education. Learning factories provide suitable learning environment [3], especially to overcome possible social and technological barriers [4]. Some processes and process chains have already been implemented in learning factories, but their focus is mainly on manufacturing processes [5]. Cooling processes have not yet been extensively introduced and taught in the context of CPPS in learning factories [6]. Cooling processes are not only very important in industry, but also very
∗Corresponding author. Tel.: +49 531 391-7622 ; fax: +49 531 391-5842. E-mail address: marcus.vogt@tu-braunschweig.de
2351-9789 c 2019 The Authors. Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the 9thConference on Learning Factories.
2 M. Vogt et al. / Procedia Manufacturing 00 (2019) 000–000
energy-intensive. Conditioning of buildings, industrial processes and mobile cooling are some major applications of cooling processes to ensure a closed cooling chain [7–9]. In general, operators and other stakeholders are strongly interested in improving energy efficiency and reliability in order to save costs [10] and reduce environmental impact. In practice, cooling systems often do not provide comprehensive information transparency and automated decision support is rarely possible. Here, CPPS approaches can be beneficial. The digital representation of the reality leads to greater transparency and automated decision support [11].
This paper presents a cyber-physical cooling storage station, called fridge demonstrator (FRED), which is inte-grated into an existing learning factory. Furthermore, the potential of FRED for engineering education and possible applications for teaching are analysed. FRED is not only capable of cooling down small work pieces, but can also store and retrieve them. Moreover, students can integrate their own modelling approaches to predict the cooling times. In summary, working with the system learners have the chance to learn more about thermodynamic processes, industrial sensors and industrial automation and deepen their knowledge in laboratory exercises.
2. Cyber-physical production systems (CPPS) in learning factories
Due to the rapid development of the information and communication technology (ICT), a large number of its appli-cations in industry and research have become possible [4]. In general, a CPPS consists of four main elements: physical world, data acquisition, cyber world, and visualisation & control. To be at par with the advancements of the ICT, em-ployees and the future manufacturing engineers need to be educated adequately [3]. To impart key competencies for this development, learning factories are seen as suitable learning environments [3]. Numerous authors consider en-ergy efficiency to be a meaningful field taught in learning factories [12–14], however, literature reveals that enen-ergy efficiency is rarely taught against the background of digitalisation in production engineering [15]. The present CPPS demonstrates the use of learning factories for teaching and research by integrating energy efficiency and resource effi-ciency. CPPSs are successful in transferring knowledge to students, which is in accordance with the blended learning concept [16]. In this concept, students actively participate in hands-on education [17], which leads to higher learning motivation and success [18]. At Technische Universität Braunschweig, concepts for the design of learning environ-ments for teaching energy efficiency and energy transparency have already been developed [19]. However, to the best of authors’ knowledge, currently there is no example covering topics of energy efficiency and transparency in practical courses and team projects. Therefore, the development of such production processes is important.
3. Concept of FRED
Motivation and learning success of engineering students can be improved by combining research-based learning approaches and utilisation of physical infrastructure [18]. "Die Lernfabrik" at Technische Universität Braunschweig follows this approach. This learning factory has three parts Research Lab, Experience Lab and Education Lab. The practical part of the course "Sustainable Cyber Physical Production Systems" takes place in the Experience Lab and focusses on the fundamentals of CPPS and its application, in order to foster sustainability in production engineer-ing [4]. This paper presents the development of a refrigerated storage station, meanengineer-ing a fridge demonstrator (FRED), which is integrated into the Experience Lab. The suitability of FRED for education in learning factories is evaluated using the assessment framework introduced by Thiede et al. [4]. The authors of this framework conclude that there are three distinct levels (I to III) of CPPS. A CPPS of level III has the highest degree of suitability for educational purposes. Applying this framework, the suitability of a technical system for teaching can be assessed. Each of the four elements of a CPPS is evaluated. A CPPS can reach a maximum of twelve points, implying a high suitability for education.
3.1. Implementation of a CPPS
The CPPS elements of FRED are shown in figure 1 (b). Starting from the physical world, the CPPS performs two main tasks: first, cool down the work pieces (WP) within a certain amount of time and second, store the work pieces in a certain position on the trays of the storage rack. To achieve the first task, thermoelectric cooling units, peltier
144 Marcus Vogt et al. / Procedia Manufacturing 31 (2019) 142–147
M. Vogt et al. / Procedia Manufacturing 00 (2019) 000–000 3
elements (PE), are employed. As all characterising state variables and control parameters of the system are known, a level III in the physical world is reached.
(a) (b) (c)
4) Feedback & control 2) Data
acquisition
• inside temperature and humidity • temperature of WP
Learners in focus 1) Physical world
• current applied to peltier elements • speed of ventilators
• geometry of FRED, work pieces • robotics
• control strategie for
storage and retrieval • temperature of work pieces
• surface temperature of peltier elements
• SQL database
3) Cyber world • thermal modelling of cooling • modelling of temperature
gradient
temperature high
low
Fig. 1. (a) Photo of FRED; (b) CPPS of the use case; (c) Exemplary visualisation of temperature gradient in station.
In order to integrate data acquisition, additional sensors are attached to the storage rack. This enables the temperature measurements of work pieces, peltier elements’ surface and the inside. All measured data is stored in a SQL database and the contained data is displayed by a visualisation interface. For the inside temperature of the storage rack, it is also possible to measure the spatial distribution, to display the temperature layering. Thus, it is concluded that level III in terms of data acquisition is reached.
In the cyber world, the cooling process is virtually replicated in order to provide decision support for the control system. Different model-based approaches can be integrated to represent the cooling process in FRED. These are look-up tables based on qualitative correlations, physical models and numerical models that describe the exact cooling process in spatial resolution [20]. Most of the mentioned modelling approaches have calculation times of a few seconds and the required data gathering takes also only a few seconds. Thus, FRED represents a CPPS of level III in the cyber world.
Feedback & control is executed by a Programmable Logic Controller (PLC). The PLC controls sensors and the
robotics, including the fully automated gripper arm and motors. The WPs’ temperature is measured at the time of their retrieval also. In the case of a deviation of the predicted and measured temperature, the cooling time calculation can be adapted. The supporting visualisation allows human machine interaction and also offers transparency of important variables and parameters of the control loop. Due to the high degree of automation and the possibility to integrate different cyber models, a level III results in the area of feedback & control.
Based on the described examination, FRED is very suitable to be integrated into teaching and research-based learning, according to the assessment framework introduced by Thiede et al. [4].
4. Implementation of FRED in "Die Lernfabrik" at Technische Universität Braunschweig
The developed station, FRED, enables students to apply, test and develop methods and tools related to CPPS. The cooling station is seamlessly integrated into the existing learning factory. Since an oven station is already integrated in the learning factory [4], the cooling station is placed behind the oven station to ensure different requirements for cooling, see subsection 4.2. In this section, different tasks for students and possible learning objectives are presented. A photograph of the real station and a technical drawing of the station can be seen in Fig. 1.
4 M. Vogt et al. / Procedia Manufacturing 00 (2019) 000–000 4.1. Analysing operation of peltier elements (PE)
Parameters, such as current applied to the peltier elements (PE), significantly affect their operation and coefficient of performance (COP). Besides the PE itself, learners can also affect the heat transfer at the cold and hot side of the PE by changing the speed of the attached ventilators. Since the evacuated heat on both sides is of huge importance for the operation of the PE, learners can influence the energetic mode of operation of the system in various kinds. To evaluate energy efficiency, different key performance indicators (KPI) can be calculated. One of which is the average energy per work piece needed to cool down to a fixed target temperature:
KPIEE,WP= N i=1 ti,c ti,in Ptotal(t)dt N ≈ N i=1
Ptotal·tc,i− tin,i
N = N i=1 KPIEE,WP,i N (1)
Ptotal(t) is the total electric power to cool down work piece i; and N is the total number of work pieces entering the
station over a time. Ptotalincludes not only the power of the PEs, but also the power required for the fans. Work pieces
enter at time tin,iand are retrieved at tc,i. The cooling time of a WP is computed as:
∆tc,i=tc,i− tin,i ; (s) (2)
By varying the mode of operation of the system, learners can calculate the effect on the introduced KPI and evaluate the resulting energy efficiency of the system.
4.2. Cooling requirements and product quality
Different products have different requirements regarding their cooling strategy (see table 1). In order to guarantee product quality, these requirements have to be fulfilled. The work pieces are made of red or black plastic or aluminium. These materials represent different products, which entails that different cooling strategies are needed to maintain their quality.
Table 1. Different requirements for cooling depending on the product.
Product (P) Requirement for cooling Exemplary real-world application
red plastic (rp) cool down as fast as possible, from initial temperature (Tini) to target
tem-perature (Tt) ambient temperature (Ta)
shock-freezing of food
black plastic (bp) cool down in any time from Tinito Tt preparing food for packaging or temperature
critical processes
aluminium (al) cool down in any time from Tinito Taas fast as required accelerating natural cooling
However, since these requirements have to be fulfilled as better as possible, a scoring system is applied to evaluate the achieved product quality. Learners might also come up with control strategies, which consider relocating work pieces during their cooling.
4.3. 3D temperature gradient
The heat exchangers are placed on top of each other. The limited air circulation inside the station; and the place-ment of the entrance and exit doors for the work pieces cause temperature gradients inside the station. Since real-world applications often face the same challenge, it is of common interest to understand how temperature within a defined volume is distributed. Even though low-cost sensors are increasingly available, yet 3D monitoring of temperature and humidity is difficult. The proposed concept can overcome this challenge, as the robot is equipped with dedicated sensors, which allow 3D measurement of temperature and humidity. For doing so, the robot moves to different loca-tions inside the storage rack and measures the temperature and humidity at different localoca-tions. Different modelling approaches can be considered by taking the temperature gradient into account.
elements (PE), are employed. As all characterising state variables and control parameters of the system are known, a level III in the physical world is reached.
(a) (b) (c)
4) Feedback & control 2) Data
acquisition
• inside temperature and humidity • temperature of WP
Learners in focus 1) Physical world
• current applied to peltier elements • speed of ventilators
• geometry of FRED, work pieces • robotics
• control strategie for
storage and retrieval • temperature of work pieces
• surface temperature of peltier elements
• SQL database
3) Cyber world • thermal modelling of cooling • modelling of temperature
gradient
temperature high
low
Fig. 1. (a) Photo of FRED; (b) CPPS of the use case; (c) Exemplary visualisation of temperature gradient in station.
In order to integrate data acquisition, additional sensors are attached to the storage rack. This enables the temperature measurements of work pieces, peltier elements’ surface and the inside. All measured data is stored in a SQL database and the contained data is displayed by a visualisation interface. For the inside temperature of the storage rack, it is also possible to measure the spatial distribution, to display the temperature layering. Thus, it is concluded that level III in terms of data acquisition is reached.
In the cyber world, the cooling process is virtually replicated in order to provide decision support for the control system. Different model-based approaches can be integrated to represent the cooling process in FRED. These are look-up tables based on qualitative correlations, physical models and numerical models that describe the exact cooling process in spatial resolution [20]. Most of the mentioned modelling approaches have calculation times of a few seconds and the required data gathering takes also only a few seconds. Thus, FRED represents a CPPS of level III in the cyber world.
Feedback & control is executed by a Programmable Logic Controller (PLC). The PLC controls sensors and the
robotics, including the fully automated gripper arm and motors. The WPs’ temperature is measured at the time of their retrieval also. In the case of a deviation of the predicted and measured temperature, the cooling time calculation can be adapted. The supporting visualisation allows human machine interaction and also offers transparency of important variables and parameters of the control loop. Due to the high degree of automation and the possibility to integrate different cyber models, a level III results in the area of feedback & control.
Based on the described examination, FRED is very suitable to be integrated into teaching and research-based learning, according to the assessment framework introduced by Thiede et al. [4].
4. Implementation of FRED in "Die Lernfabrik" at Technische Universität Braunschweig
The developed station, FRED, enables students to apply, test and develop methods and tools related to CPPS. The cooling station is seamlessly integrated into the existing learning factory. Since an oven station is already integrated in the learning factory [4], the cooling station is placed behind the oven station to ensure different requirements for cooling, see subsection 4.2. In this section, different tasks for students and possible learning objectives are presented. A photograph of the real station and a technical drawing of the station can be seen in Fig. 1.
4.1. Analysing operation of peltier elements (PE)
Parameters, such as current applied to the peltier elements (PE), significantly affect their operation and coefficient of performance (COP). Besides the PE itself, learners can also affect the heat transfer at the cold and hot side of the PE by changing the speed of the attached ventilators. Since the evacuated heat on both sides is of huge importance for the operation of the PE, learners can influence the energetic mode of operation of the system in various kinds. To evaluate energy efficiency, different key performance indicators (KPI) can be calculated. One of which is the average energy per work piece needed to cool down to a fixed target temperature:
KPIEE,WP= N i=1 ti,c ti,in Ptotal(t)dt N ≈ N i=1
Ptotal·tc,i− tin,i
N = N i=1 KPIEE,WP,i N (1)
Ptotal(t) is the total electric power to cool down work piece i; and N is the total number of work pieces entering the
station over a time. Ptotalincludes not only the power of the PEs, but also the power required for the fans. Work pieces
enter at time tin,iand are retrieved at tc,i. The cooling time of a WP is computed as:
∆tc,i=tc,i− tin,i ; (s) (2)
By varying the mode of operation of the system, learners can calculate the effect on the introduced KPI and evaluate the resulting energy efficiency of the system.
4.2. Cooling requirements and product quality
Different products have different requirements regarding their cooling strategy (see table 1). In order to guarantee product quality, these requirements have to be fulfilled. The work pieces are made of red or black plastic or aluminium. These materials represent different products, which entails that different cooling strategies are needed to maintain their quality.
Table 1. Different requirements for cooling depending on the product.
Product (P) Requirement for cooling Exemplary real-world application
red plastic (rp) cool down as fast as possible, from initial temperature (Tini) to target
tem-perature (Tt) ambient temperature (Ta)
shock-freezing of food
black plastic (bp) cool down in any time from Tinito Tt preparing food for packaging or temperature
critical processes
aluminium (al) cool down in any time from Tinito Taas fast as required accelerating natural cooling
However, since these requirements have to be fulfilled as better as possible, a scoring system is applied to evaluate the achieved product quality. Learners might also come up with control strategies, which consider relocating work pieces during their cooling.
4.3. 3D temperature gradient
The heat exchangers are placed on top of each other. The limited air circulation inside the station; and the place-ment of the entrance and exit doors for the work pieces cause temperature gradients inside the station. Since real-world applications often face the same challenge, it is of common interest to understand how temperature within a defined volume is distributed. Even though low-cost sensors are increasingly available, yet 3D monitoring of temperature and humidity is difficult. The proposed concept can overcome this challenge, as the robot is equipped with dedicated sensors, which allow 3D measurement of temperature and humidity. For doing so, the robot moves to different loca-tions inside the storage rack and measures the temperature and humidity at different localoca-tions. Different modelling approaches can be considered by taking the temperature gradient into account.
146 Marcus Vogt et al. / Procedia Manufacturing 31 (2019) 142–147
M. Vogt et al. / Procedia Manufacturing 00 (2019) 000–000 5
4.4. Development of relocating logics
The proposed FRED has six shelves and each shelve has slots to accommodate eight work pieces. The six shelves are also placed on the top of each other. The described temperature gradient inside FRED leads to different cooling times depending on the level. However, work pieces placed close to the cooling elements are expected to have shorter cooling times. As mentioned before, there are different requirements for cooling depending on the product family P. In practical courses, students are asked to cool row of work pieces from different product families. Consequently, it is essential to be aware of both, the placement of work pieces and their appearance in the production sequence. Students might also consider to relocate certain work pieces during the cooling process using the robot.
4.5. Overall learning objectives
The above mentioned sections represent different tasks on which students work on during the "Sustainable Cyber Physical Production Systems" course. Within the practical part of this course, the goal of FRED is to enable students to understand a CPPS using the example of a cooling system. As table 2 shows, students work on tasks 4.1 to 4.4. In order to succeed in a subsequent task, students are asked to work on other tasks before. For instance, task 4.3 requires the completion of task 4.1 and 4.2. Each task addresses different topics and has a distinct learning outcome. After completing task 4.4, students have gained basic knowledge in all addressed topics, but more importantly, they will understand potentials of a fully implemented CPPS.
Table 2. Overall learning objectives of FRED.
Task Dependencies Addressed topics Learning outcome
4.1 Analysing operation of peltier elements
none energy efficiency, measurement understanding different modes of operation of a simple technical system
4.2 Cooling requirements and product quality
none heat transfer, material properties development of material-specific cooling model
4.3 3D temperature gradient 4.1 and 4.2 3D measurement, data analytics development of spatial temperature moni-toring system
4.4 Development of relocat-ing logics
4.3 programming of robot, systems
en-gineering
understanding of CPPS potentials
5. Conclusion and outlook
This paper presents a cyber-physical cooling storage station, which is integrated into an existing learning factory. The potential for its use in engineering education is analysed. The proposed cooling system design enables innovative visualisation and user interaction for learners. This system allows learners to experience the interaction of thermodynamic processes, industrial sensors and industrial automation to deepen their knowledge in laboratory exercises. The overall learning objective of the cooling storage system is to understand the system behaviour and evaluate relevant KPIs, which account for an efficient cooling process and product quality. The station is capable of measuring the inner 3D temperature. Thus, learners can apply models, which take temperature gradients into account. A visualisation interface facilitates the integration of such models. In summary, the introduced cooling storage station proves to be an appropriate learning environment to apply methods related to CPPS in engineering education.
In future work, the station can be further improved by establishing mixed reality. This innovative visualisation technique could show inside temperature gradient and even vector fields of the circulating air. This can make the physical behaviour of the station more understandable. It is planned to set up the station at our partner’s learning factory at BITS, Pilani. By combining the data of both stations, learners can also experience the impact of changing
6 M. Vogt et al. / Procedia Manufacturing 00 (2019) 000–000
the outer environmental conditions, e.g. temperature and humidity. By integrating metrics, such as energy costs and local power mix, learners can also compare their results received from both learning factories. The existing learning factory at Technische Universität Braunschweig also owns an oven station. The presented cooling process generates a lot of waste energy (three times more than cooling power), which can be used to preheat the oven station. The synergistic interconnection of processes can be demonstrated through this modification.
Acknowledgements
The development of the presented demonstrator for this paper was funded by the German Federal Ministry of Education and Research (BMBF) under grant number 01IS17006C. The authors also wish to thank Atul Jala and Manpreet Singh from Birla Institute of Technology and Science, Pilani, India. During their DAAD funded (project "JInGEL – Joint Indo German Experience Lab", project ID 57219215) stay at Technische Universität Braunschweig, both students have intensively supported the development, construction and improvement of the presented station. They and Lars Kostrewa made an important contribution to the realisation of this station.
References
[1] D. Chen, S. Heyer, S. Ibbotson, K. Salonitis, J. G. Steingrímsson, and S. Thiede. Direct digital manufacturing: definition, evolution, and sustainability implications. Journal of Cleaner Production, 107:615–625, 2015. doi: 10.1016/j.jclepro.2015.05.009.
[2] Forschungsunion Wirtschaft und Wissenschaft. Im Fokus das Zukunftsprojekt Industrie 4.0; Handlungsempfehlungen zur Umsetzung. [3] M. Tisch, C. Hertle, J. Cachay, E. Abele, J. Metternich, and R. Tenberg. A Systematic Approach on Developing Action-oriented,
Competency-based Learning Factories. Procedia CIRP, 7:580–585, 2013. doi: 10.1016/j.procir.2013.06.036.
[4] S. Thiede, M. Juraschek, and C. Herrmann. Implementing Cyber-physical Production Systems in Learning Factories. Procedia CIRP, 54:7–12, 2016. doi: 10.1016/j.procir.2016.04.098.
[5] E. Abele, J. Metternich, M. Tisch, G. Chryssolouris, W. Sihn, H. ElMaraghy, V. Hummel, and F. Ranz. Learning Factories for Research, Education, and Training. Procedia CIRP, 32:1–6, 2015. doi: 10.1016/j.procir.2015.02.187.
[6] Y. Yang, X. Li, Z. Yang, Q. Wei, N. Wang, and L. Wang. The Application of Cyber Physical System for Thermal Power Plants: Data-Driven Modeling. Energies, 11(4):690, 2018. doi: 10.3390/en11040690.
[7] M. Jakubcionis and J. Carlsson. Estimation of European Union residential sector space cooling potential. Energy Policy, 101:225–235, 2017. doi: 10.1016/j.enpol.2016.11.047.
[8] J. Nunes, P. D. Silva, L. Domingues, L. P. Andrade, and P. D. Gaspar. Energy evaluation of refrigeration systems in portuguese fruits and vegetables industry. International Conference on Sustainability and the Cold Chain, 2014, 2014. doi: 10.13140/2.1.1360.0647.
[9] A. Gallo, R. Accorsi, G. Baruffaldi, and R. Manzini. Designing Sustainable Cold Chains for Long-Range Food Distribution: Energy-Effective Corridors on the Silk Road Belt. Sustainability, 9(11):2044, 2017. doi: 10.3390/su9112044.
[10] T. Zachariadis, A. Michopoulos, Y. Vougiouklakis, K. Piripitsi, C. Ellinopoulos, and B. Struss. Determination of Cost-Effective Energy Efficiency Measures in Buildings with the Aid of Multiple Indices. Energies, 11(1):191, 2018. doi: 10.3390/en11010191.
[11] J. Klöber-Koch, J. Pielmeier, S. Grimm, M. Miliˇci´c Brandt, M. Schneider, and G. Reinhart. Knowledge-Based Decision Making in a Cyber-Physical Production Scenario. Procedia Manufacturing, 9:167–174, 2017. doi: 10.1016/j.promfg.2017.04.014.
[12] E. Abele, D. Flum, and N. Strobel. A Systematic Approach for Designing Learning Environments for Energy Efficiency in Industrial Produc-tion. Procedia Manufacturing, 9:9–16, 2017. doi: 10.1016/j.promfg.2017.04.001.
[13] H. Karre, M. Hammer, and C. Ramsauer. Learn how to cope with volatility in operations at Graz University of Technology’s LEAD Factory. Procedia Manufacturing, 23:15–20, 2018. doi: 10.1016/j.promfg.2018.03.154.
[14] C. Faller and D. Feldmüller. Industry 4.0 Learning Factory for regional SMEs. Procedia CIRP, 32:88–91, 2015. doi: 10.1016/j.procir.2015.02.117.
[15] L. Büth, S. Blume, G. Posselt, and C. Herrmann. Training concept for and with digitalization in learning factories: An energy efficiency training case. Procedia Manufacturing, 23:171–176, 2018. doi: 10.1016/j.promfg.2018.04.012.
[16] H. M. Anderson. Dale’s Cone of Experience, n.a. URL http://www.queensu.ca/teachingandlearning/modules/active/documents/Dales_Cone_ of_Experience_summary.pdf.
[17] T. H.-J. Uhlemann, C. Schock, C. Lehmann, S Freiberger, and R. Steinhilper. The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems. Procedia Manufacturing, 9:113–120, 2017. doi: 10.1016/j.promfg.2017.04.043.
[18] S. Blume, N. Madanchi, S. Böhme, G. Posselt, S. Thiede, and C. Herrmann. Die Lernfabrik – Research-based Learning for Sustainable Production Engineering. Procedia CIRP, 32:126–131, 2015. doi: 10.1016/j.procir.2015.02.113.
[19] A. Kaluza, M. Juraschek, B. Neef, R. Pittschelllis, G. Posselt, S Thiede, and C. Herrmann. Designing Learning Environments for Energy Efficiency through Model Scale Production Processes. Procedia CIRP, 32:41–46, 2015. doi: 10.1016/j.procir.2015.02.114.
[20] G. Posselt, P. Booij, S. Thiede, J. Fransman, B. Driessen, and C. Herrmann. 3d Thermal Climate Monitoring in Factory Buildings. Procedia CIRP, 29:98–103, 2015. doi: 10.1016/j.procir.2015.02.178.
4.4. Development of relocating logics
The proposed FRED has six shelves and each shelve has slots to accommodate eight work pieces. The six shelves are also placed on the top of each other. The described temperature gradient inside FRED leads to different cooling times depending on the level. However, work pieces placed close to the cooling elements are expected to have shorter cooling times. As mentioned before, there are different requirements for cooling depending on the product family P. In practical courses, students are asked to cool row of work pieces from different product families. Consequently, it is essential to be aware of both, the placement of work pieces and their appearance in the production sequence. Students might also consider to relocate certain work pieces during the cooling process using the robot.
4.5. Overall learning objectives
The above mentioned sections represent different tasks on which students work on during the "Sustainable Cyber Physical Production Systems" course. Within the practical part of this course, the goal of FRED is to enable students to understand a CPPS using the example of a cooling system. As table 2 shows, students work on tasks 4.1 to 4.4. In order to succeed in a subsequent task, students are asked to work on other tasks before. For instance, task 4.3 requires the completion of task 4.1 and 4.2. Each task addresses different topics and has a distinct learning outcome. After completing task 4.4, students have gained basic knowledge in all addressed topics, but more importantly, they will understand potentials of a fully implemented CPPS.
Table 2. Overall learning objectives of FRED.
Task Dependencies Addressed topics Learning outcome
4.1 Analysing operation of peltier elements
none energy efficiency, measurement understanding different modes of operation of a simple technical system
4.2 Cooling requirements and product quality
none heat transfer, material properties development of material-specific cooling model
4.3 3D temperature gradient 4.1 and 4.2 3D measurement, data analytics development of spatial temperature moni-toring system
4.4 Development of relocat-ing logics
4.3 programming of robot, systems
en-gineering
understanding of CPPS potentials
5. Conclusion and outlook
This paper presents a cyber-physical cooling storage station, which is integrated into an existing learning factory. The potential for its use in engineering education is analysed. The proposed cooling system design enables innovative visualisation and user interaction for learners. This system allows learners to experience the interaction of thermodynamic processes, industrial sensors and industrial automation to deepen their knowledge in laboratory exercises. The overall learning objective of the cooling storage system is to understand the system behaviour and evaluate relevant KPIs, which account for an efficient cooling process and product quality. The station is capable of measuring the inner 3D temperature. Thus, learners can apply models, which take temperature gradients into account. A visualisation interface facilitates the integration of such models. In summary, the introduced cooling storage station proves to be an appropriate learning environment to apply methods related to CPPS in engineering education.
In future work, the station can be further improved by establishing mixed reality. This innovative visualisation technique could show inside temperature gradient and even vector fields of the circulating air. This can make the physical behaviour of the station more understandable. It is planned to set up the station at our partner’s learning factory at BITS, Pilani. By combining the data of both stations, learners can also experience the impact of changing
the outer environmental conditions, e.g. temperature and humidity. By integrating metrics, such as energy costs and local power mix, learners can also compare their results received from both learning factories. The existing learning factory at Technische Universität Braunschweig also owns an oven station. The presented cooling process generates a lot of waste energy (three times more than cooling power), which can be used to preheat the oven station. The synergistic interconnection of processes can be demonstrated through this modification.
Acknowledgements
The development of the presented demonstrator for this paper was funded by the German Federal Ministry of Education and Research (BMBF) under grant number 01IS17006C. The authors also wish to thank Atul Jala and Manpreet Singh from Birla Institute of Technology and Science, Pilani, India. During their DAAD funded (project "JInGEL – Joint Indo German Experience Lab", project ID 57219215) stay at Technische Universität Braunschweig, both students have intensively supported the development, construction and improvement of the presented station. They and Lars Kostrewa made an important contribution to the realisation of this station.
References
[1] D. Chen, S. Heyer, S. Ibbotson, K. Salonitis, J. G. Steingrímsson, and S. Thiede. Direct digital manufacturing: definition, evolution, and sustainability implications. Journal of Cleaner Production, 107:615–625, 2015. doi: 10.1016/j.jclepro.2015.05.009.
[2] Forschungsunion Wirtschaft und Wissenschaft. Im Fokus das Zukunftsprojekt Industrie 4.0; Handlungsempfehlungen zur Umsetzung. [3] M. Tisch, C. Hertle, J. Cachay, E. Abele, J. Metternich, and R. Tenberg. A Systematic Approach on Developing Action-oriented,
Competency-based Learning Factories. Procedia CIRP, 7:580–585, 2013. doi: 10.1016/j.procir.2013.06.036.
[4] S. Thiede, M. Juraschek, and C. Herrmann. Implementing Cyber-physical Production Systems in Learning Factories. Procedia CIRP, 54:7–12, 2016. doi: 10.1016/j.procir.2016.04.098.
[5] E. Abele, J. Metternich, M. Tisch, G. Chryssolouris, W. Sihn, H. ElMaraghy, V. Hummel, and F. Ranz. Learning Factories for Research, Education, and Training. Procedia CIRP, 32:1–6, 2015. doi: 10.1016/j.procir.2015.02.187.
[6] Y. Yang, X. Li, Z. Yang, Q. Wei, N. Wang, and L. Wang. The Application of Cyber Physical System for Thermal Power Plants: Data-Driven Modeling. Energies, 11(4):690, 2018. doi: 10.3390/en11040690.
[7] M. Jakubcionis and J. Carlsson. Estimation of European Union residential sector space cooling potential. Energy Policy, 101:225–235, 2017. doi: 10.1016/j.enpol.2016.11.047.
[8] J. Nunes, P. D. Silva, L. Domingues, L. P. Andrade, and P. D. Gaspar. Energy evaluation of refrigeration systems in portuguese fruits and vegetables industry. International Conference on Sustainability and the Cold Chain, 2014, 2014. doi: 10.13140/2.1.1360.0647.
[9] A. Gallo, R. Accorsi, G. Baruffaldi, and R. Manzini. Designing Sustainable Cold Chains for Long-Range Food Distribution: Energy-Effective Corridors on the Silk Road Belt. Sustainability, 9(11):2044, 2017. doi: 10.3390/su9112044.
[10] T. Zachariadis, A. Michopoulos, Y. Vougiouklakis, K. Piripitsi, C. Ellinopoulos, and B. Struss. Determination of Cost-Effective Energy Efficiency Measures in Buildings with the Aid of Multiple Indices. Energies, 11(1):191, 2018. doi: 10.3390/en11010191.
[11] J. Klöber-Koch, J. Pielmeier, S. Grimm, M. Miliˇci´c Brandt, M. Schneider, and G. Reinhart. Knowledge-Based Decision Making in a Cyber-Physical Production Scenario. Procedia Manufacturing, 9:167–174, 2017. doi: 10.1016/j.promfg.2017.04.014.
[12] E. Abele, D. Flum, and N. Strobel. A Systematic Approach for Designing Learning Environments for Energy Efficiency in Industrial Produc-tion. Procedia Manufacturing, 9:9–16, 2017. doi: 10.1016/j.promfg.2017.04.001.
[13] H. Karre, M. Hammer, and C. Ramsauer. Learn how to cope with volatility in operations at Graz University of Technology’s LEAD Factory. Procedia Manufacturing, 23:15–20, 2018. doi: 10.1016/j.promfg.2018.03.154.
[14] C. Faller and D. Feldmüller. Industry 4.0 Learning Factory for regional SMEs. Procedia CIRP, 32:88–91, 2015. doi: 10.1016/j.procir.2015.02.117.
[15] L. Büth, S. Blume, G. Posselt, and C. Herrmann. Training concept for and with digitalization in learning factories: An energy efficiency training case. Procedia Manufacturing, 23:171–176, 2018. doi: 10.1016/j.promfg.2018.04.012.
[16] H. M. Anderson. Dale’s Cone of Experience, n.a. URL http://www.queensu.ca/teachingandlearning/modules/active/documents/Dales_Cone_ of_Experience_summary.pdf.
[17] T. H.-J. Uhlemann, C. Schock, C. Lehmann, S Freiberger, and R. Steinhilper. The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems. Procedia Manufacturing, 9:113–120, 2017. doi: 10.1016/j.promfg.2017.04.043.
[18] S. Blume, N. Madanchi, S. Böhme, G. Posselt, S. Thiede, and C. Herrmann. Die Lernfabrik – Research-based Learning for Sustainable Production Engineering. Procedia CIRP, 32:126–131, 2015. doi: 10.1016/j.procir.2015.02.113.
[19] A. Kaluza, M. Juraschek, B. Neef, R. Pittschelllis, G. Posselt, S Thiede, and C. Herrmann. Designing Learning Environments for Energy Efficiency through Model Scale Production Processes. Procedia CIRP, 32:41–46, 2015. doi: 10.1016/j.procir.2015.02.114.
[20] G. Posselt, P. Booij, S. Thiede, J. Fransman, B. Driessen, and C. Herrmann. 3d Thermal Climate Monitoring in Factory Buildings. Procedia CIRP, 29:98–103, 2015. doi: 10.1016/j.procir.2015.02.178.
